Published Release Notes

New interaction history attribute

Pega^® Platform 7.4 introduces the pySubjectType attribute that is used in interaction history aggregations. This attribute is populated for interaction history records that were created in release 7.4. For records that originated in earlier releases, the attribute must be set in the following scenarios:

• Single-level decisioning frameworks that use interaction history.
• Multi-level decisioning frameworks where interaction history is used by two or more levels of strategies that are defined on different classes.

For the single-level scenario, configure the Dynamic System Setting that sets the pySubjectType attribute when your framework reads interaction history records. The value of this Dynamic System Setting becomes the name of the customer class.

For the multi-level scenario, update the database table for all strategy levels manually. For each level, make sure that the value in the Subject Type column is set to the name of the class for the corresponding strategy. For example, the value for the top level strategy should be set to the name of the class of that strategy.

For more information about interaction history aggregations, see Data Sources landing page

For more information about multi-line strategies and contexts, see Strategy components - Embedded strategy

HBase data set type becomes resumable

HBase data set has been enhanced to be resumable. As a result, data flow runs that reference the HBase data set as their primary data source are now resumable. You can pause and resume such data flow runs.

For more information, see the Resumability of data flow runs section in Data flow run updates.

Upgrading Adaptive Decision Manager data mart tables might fail

Issue: Upgrade from 7.3 to 7.3.1 fails if the data contained in the pxInsName column of the PR_DATA_DM_ADMMART_PRED_FACT table is longer than 128 characters.

Reason: During the Pega Platform™ upgrade from 7.3 to 7.3.1, data in the Adaptive Decision Manager (ADM) data mart tables is migrated from the PR_DATA_DM_ADMMART_PRED_FACT table to the PR_DATA_DM_ADMMART_MDL_FACT table. In Pega 7.3.1, ADM uses only the PR_DATA_DM_ADMMART_MDL_FACT table where the pxInsName property can store values that are 128 characters long. In Pega Platform 7.3, the pxInsName property in the PR_DATA_DM_ADMMART_PRED_FACT table can store values that are 255 characters long. If the pxInsName property contains values that are longer that 128 characters, the upgrade fails.

Resolution: Issue an ALTER TABLE statement to change the pxInsName column size to 255 characters and resume the upgrade. For example:

ALTER TABLE rules.pr_data_dm_admmart_pred ALTER COLUMN pxInsName TYPE varchar(255);

For more information, see Adaptive Decision Manager data model.

Support for predictive models in PMML version 4.4

Pega Platform™ now supports the import of predictive models in Predictive Model Markup Language (PMML) version 4.4. With this feature, you can import PMML models that use the anomaly detection algorithm.

For a list of all supported PMML models, see Supported models for import. 

Condition builder enhancements

To enhance the precision of condition builder in both App Studio and Dev Studio, the new instances include comparator helps you specify the number of field group and field group list instances to which a when rule applies. In addition, a search option that returns results on keypress helps you easily look for existing values.

For more information, see Create conditions in an enhanced condition builder (8.5), Defining conditions in the condition builder.

Limits on active data flow runs

You can now configure a maximum number of concurrent active data flow runs for a node type. Set limits to ensure that you do not run out of system resources and that you have a reasonable processing throughput. If a limit is reached, the system queues subsequent runs and waits for active runs to stop or finish before queued runs can be initiated, starting with the oldest.

For more information see, Limit the number of active runs in data flow services (8.5).

Upgrade impact

If you have many data flow runs active at the same time, you might notice that some of the runs are queued and waiting to be executed.

What steps are required to update the application to be compatible with this change?

You do not have to take any action. After the active runs stop or finish, the queued runs start automatically. The default limits are intended to protect your system resources, and you should not see a negative impact on the processing of data flows. However, if you want to allow a greater number of active data flow runs to be active at the same time, you can change the limits. For more information, see Limiting active data flow runs.

Support for Apache HBase 2.1 and Hadoop 3.0

Support for these versions extends Pega Platform™ compatibility with HBase releases to ensure that your database implementations integrate seamlessly with Pega Platform.

Pega Platform now supports:

• Apache HBase 2.1 for the HBase data set
• Apache Hadoop Distributed File System (HDFS) 3.0 for the HDFS data set

For more information, see Enhance your data sets with Apache HBase 2.1 and Hadoop 3.0 (8.5).

Enhancing your revision management process with Deployment Manager pipelines

Pega Platform 8.5 offers improved synergy between revision management and the automated deployment process provided by Pega's Deployment Manager 4.8 pipelines. Use Deployment Manager 4.8 to increase the efficiency of business-as-usual application changes and automatize the deployment of revision packages.

For more information, see Managing the business-as-usual changes.

Support for Cloud AutoML topic detection models

In Prediction Studio, you can now connect to topic detection models that you create in Cloud AutoML, Google's cloud-based machine learning service. You can then use the models to categorize and route messages from your customers.

For more information, see Broaden your selection of topic detection models by connecting to third-party services (8.5).

Control group configuration for predictions

You can now configure a control group for your predictions in Prediction Studio. Based on the control group, Prediction Studio calculates a lift score for each prediction that you can later use to monitor the success rate of your predictions.

For more information, see Customizing predictions.